This project aims to analyze Netflix’s content dataset to answer key questions about its trends and composition. Specifically, we are seeking answers to the following questions:
- How has the dominance of genres changed by the year?
- How are the top 5 genres distributed across the top 5 countries with the most Netflix titles?
- What are the trends of TV shows and movies over the years, and what factors might influence these trends?
Data Source
To explore the above questions, we used a publicly available dataset from Kaggle:
Netflix movies and TV shows:
https://www.kaggle.com/datasets/shivamb/netflix-shows2
Both datasets provide structured information about the content available on Netflix, allowing us to explore trends in the diversity and volume of titles on Netflix.
Ethical Questions and Limitations
Data privacy:
These datasets are publicly available and aggregate information about Netflix’s catalog, so they do not violate user privacy. However, this data may not always be up to date or accurate.
Limitations:
Data sets may not reflect real-time data or provide complete regional information, as Netflix frequently updates its productions.
Analytical bias:
Certain regions or types may be underrepresented in the data, which may affect the results of the analysis.
3 Questions with Interactive Visualizations
Loading Data
library(ggplot2)library(plotly)
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library(dplyr)
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type title director
1 TV Show 3%
2 Movie 1920 Vikram Bhatt
3 Movie 3 Heroines Iman Brotoseno
4 Movie Blue Mountain State: The Rise of Thadland Lev L. Spiro
5 TV Show Blue Planet II
6 Movie Blue Ruin Jeremy Saulnier
cast
1 João Miguel, Bianca Comparato, Michel Gomes, Rodolfo Valente, Vaneza Oliveira, Rafael Lozano, Viviane Porto, Mel Fronckowiak, Sergio Mamberti, Zezé Motta, Celso Frateschi
2 Rajneesh Duggal, Adah Sharma, Indraneil Sengupta, Anjori Alagh, Rajendranath Zutshi, Vipin Sharma, Amin Hajee, Shri Vallabh Vyas
3 Reza Rahadian, Bunga Citra Lestari, Tara Basro, Chelsea Islan
4 Alan Ritchson, Darin Brooks, James Cade, Rob Ramsay, Chris Romano, Frankie Shaw, Omari Newton, Ed Marinaro, Dhani Jones, Ed Amatrudo, Jimmy Tatro
5 David Attenborough
6 Macon Blair, Devin Ratray, Amy Hargreaves, Kevin Kolack, Eve Plumb, David W. Thompson, Brent Werzner, Stacy Rock, Sidné Anderson, Sandy Barnett, Bonnie Johnson
country date_added release_year rating duration.x
1 Brazil 14-Aug-20 2020 TV-MA 4
2 India 15-Dec-17 2008 TV-MA 143
3 Indonesia 5-Jan-19 2016 TV-PG 124
4 United States 1-Mar-16 2016 R 90
5 United Kingdom 3-Dec-18 2017 TV-G 1
6 United States, France 25-Feb-19 2013 R 90
genres
1 International TV Shows, TV Dramas, TV Sci-Fi & Fantasy
2 Horror Movies, International Movies, Thrillers
3 Dramas, International Movies, Sports Movies
4 Comedies
5 British TV Shows, Docuseries, Science & Nature TV
6 Independent Movies, Thrillers
Question 1
Question: How has the dominance of genres changed by the year?
We first made a new table that looked at the amount of all genres in the data and then selected the top five most representative genres based on the table.
From this interactive visualization graph, the comedies in 2012 are dominant compared to other genres on Netflix. Then, the dominant genre changed in 2018, dramas have dominance on Netflix. For dramas, it gradually increased from around 1995. This graph highlights how the popularity of different genres changed over the years. From 2008, the count of the top 5 genres has all rapidly boosted. Also, the trend of documentaries surged rapidly since the requirements for this type of genre’s demand increased. The count of documentaries, dramas, and comedies is rapidly alleviated because of the pandemic covid-19 and also the lack of thoroughness of data collected from 2018.
The reason why we decided to make a new interactive visualization graph for the top five genres is that it can showcase a clearer and more concise trend of the diversification of the count of the top 5 genres because of the release year.
Question 2
Question: How are the top 5 genres distributed across the top 5 countries with the most Netflix titles?
Code and Output:
library(dplyr)library(plotly)top_5_genres <-c("International Movies", "Dramas", "Comedies", "International TV Shows", "Documentaries")top_countries <- netflix_df %>%mutate(country =str_trim(country)) %>%filter(!is.na(country) & country !="") %>%count(country, sort =TRUE) %>%slice(1:5)country_genre_data <- netflix_df %>%filter( country %in% top_countries$country & genres %in% top_5_genres ) %>%count(country, genres, name ="count")plot_ly( country_genre_data,x =~country,y =~count,color =~genres,type ='bar',text =~paste("Country:", country, "<br>Genre:", genres, "<br>Count:", count),hoverinfo ="text") %>%layout(title ="Top 5 Countries with Most Netflix Titles by Top 5 Genres",xaxis =list(title ="Country"),yaxis =list(title ="Number of Titles"),barmode ='stack',legend =list(title =list(text ="Genres")) )
Brief Description:
The chart shows how Netflix titles are distributed across the top five countries with the most titles, divided into five main genres: International Movies, Dramas, Comedies, International TV Shows, and Documentaries. The United States has the most titles in all genres, showing its significant role in Netflix’s library. India comes second, with many International Movies and TV Shows showing Netflix’s focus on regional content in that country. Japan, South Korea, and the United Kingdom have smaller libraries, but their titles are spread across the genres.
International Movies and TV Shows are the most popular genres globally, showing Netflix’s effort to provide content for diverse audiences. Dramas and Documentaries are also crucial, especially in the U.S. and India. This chart highlights Netflix’s focus on the U.S. and India as key markets while showing room for growth in places like Japan and South Korea, which have strong entertainment industries.
Question 3
Question: What are the trends of TV shows and movies over the years, and what factors might influence these trends?
Code and Output:
trend_data <- netflix_df %>%filter(!is.na(release_year)) %>%group_by(release_year, type) %>%summarise(count =n(), .groups ='drop')trend_plot <-plot_ly( trend_data,x =~release_year,y =~count,color =~type,type ='scatter',mode ='lines+markers',text =~paste("Year:", release_year, "<br>Type:", type, "<br>Count:", count),hoverinfo ="text") %>%layout(title ="Trends of TV Shows and Movies Over the Years",xaxis =list(title ="Release Year"),yaxis =list(title ="Count"),legend =list(title =list(text ="Type")) )trend_plot
Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels
Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels
Brief Description:
Over the years, the production trends of TV shows and movies have undergone significant changes. Before 2000, the output of both remained relatively low and stable. After 2000, and particularly after 2010, there was a rapid increase in production. Movies peaked around 2015 before experiencing a slight decline, while TV shows continued to grow. Between 1920 and 1990, movies were the dominant form of entertainment, but after 2010, TV shows caught up and even surpassed movies in certain years. Post 2020, both TV shows and movies saw a notable decline, likely influenced by events such as the COVID-19 pandemic.
These changes can be attributed to several factors. The rise of streaming platforms like Netflix and advancements in production technology have lowered barriers, fueling growth. Audience preferences have shifted toward serialized content, driving the rise of TV shows. Additionally, TV shows’ relatively lower production costs have made them an attractive option for creators and investors. Global events like the pandemic disrupted traditional production and distribution, accelerating these trends.
Summary
By analyzing the problems we have raised, we can draw the following conclusions.
First, Netflix’s content is mainly focused on five genres: drama, comedy, documentary, international film and international TV series.
Looking at the time trend, the number of shows in these genres grew very slowly before 2000, and after 2010, perhaps with the popularity of streaming technology and Netflix’s investment in original content, the number of shows in these genres increased rapidly. Documentaries and dramas in particular have shown the most significant growth, indicating their enduring appeal among audiences. In addition, the growth of comedy and international genre content is also very evident, reflecting the increased demand for diverse and localized content from global audiences.
From the perspective of regional preference, different regions have significant differences in their preferences for program types. For example, American audiences tend to prefer comedies and documentaries, while Indian audiences prefer international dramas and dramas, and South Korean audiences show a greater interest in international dramas than other countries. This difference may be related to the cultural background and entertainment habits of each region, but also closely related to Netflix’s marketing strategy.
From the changing trend of TV shows and movies, the number of movies dominated in the early days, but in recent years, the growth rate of TV shows has obviously exceeded that of movies. This trend shows that TV shows are playing a bigger role in attracting users and increasing subscriber stickiness as viewers’ demand for long-form, continuous content increases.
In conclusion, our research reveals Netflix’s diversified content strategy in the streaming space and its global expansion trend. By analyzing program types, geographic preferences, and time shifts, documentaries and dramas dominate content, while the rapid growth of international content reflects global audiences’ demand for localized and diverse programming. The rise of TV shows is a further indication of users’ appetite for long-form content.
